In attempting to determine results in experiments, capacity sizing, predictive determination, and the like, often an iterative step approach is used to seek intermediate results for a given set of performance criteria. This iterative approach often involves having a set of predetermined characteristics or determinants, a step function with a predetermined interval step, a suite of established performance metrics, and a formula for estimating a result using some of the aforementioned determinants. Other examples in need of complex methodological results may include complex, possibly unsolvable, or difficult to solve equations and systems of which their inverses may be challenging or likely impossible to solve.
In practice however, attempting to solve an equation, process, algorithm or unknown using a given step approach is lengthy and may often consume valuable processing time, particularly for complex problems. Particularly in complex areas involving capacity sizing and research and development (R&D) estimation for instance, an iterative process typically provides a result that is not only an estimation but is often an inaccurate estimated result as well. In part, one reason for this inaccuracy is that a solution is determined only after an interval calculation “steps over” or exceeds one or more of the test guidelines thereby providing a result that is equivalent to the interval value of the iteration before the guidelines are exceeded.
Therefore, what is needed is a method of more rapidly determining a more accurate estimated resultant for one or more of a given set of performance criteria.